Merge branch 'master' of https://github.com/ankushbhatia2/docsearch

README.md

@@ -1,2 +1,34 @@
 # docsearch
-Searching similar documents in a large corpus of documents.
+It is a python library for Searching similar documents in a large corpus of documents based on my recent project.
+It uses a 2-layer Earth Mover's Distance (my research) or a Jenson Shannon Distance over latent topic distribution of documents and word embeddings. 
+
+I'll update the further methodology once my paper is published.
+
+
+## Classes
+DocSearch() :
+
+(i)__init__() takes 5 optional arguments.
+"""
+        :param n_topics: number of topics (default 100)
+        :param wv_size: word embedding dimension (default 100)
+        :param stop_words: stop words list (default list) 
+        :param min_word_freq: minimum word frequency (default 15000)
+        :param sim_metric: allowed values :['jenson-shannon', 'emd']
+"""
+
+(ii) __fit__() takes one single argument which is the list of documents.
+
+(iii) __get_most_similar_documents__() takes 2 arguments _viz._ query_document and number of similar documents to be shown(k).
+## Usage
+```from docsearch import DocSearch
+import pandas as pd
+
+docsearch = DocSearch()
+
+path = "path/to/dataset.csv"
+df = pd.read_csv(path)
+
+docsearch.fit(df['text'])
+
+print docsearch.get_most_similar_documents([str(df.at[100, 'text'])])```